Multimodal Learning Analytics (MMLA) innovations make use of rapidly evolving sensing and artificial intelligence algorithms to collect rich data about learning activities that unfold in physical learning spaces. The analysis of these data is opening exciting new avenues for both studying and supporting learning. Yet, practical and logistical challenges commonly appear while deploying MMLA innovations "in-the-wild". These can span from technical issues related to enhancing the learning space with sensing capabilities, to the increased complexity of teachers' tasks and informed consent. These practicalities have been rarely discussed. This paper addresses this gap by presenting a set of lessons learnt from a 2-year human-centred MMLA in-the-wild study conducted with 399 students and 17 educators. The lessons learnt were synthesised into topics related to i) technological/physical aspects of the deployment; ii) multimodal data and interfaces; iii) the design process; iv) participation, ethics and privacy; and v) the sustainability of the deployment.

翻译：多式学习分析(MMLA)创新利用迅速演变的遥感和人工智能算法收集关于在物理学习空间展开的学习活动的丰富数据。这些数据的分析为学习和支持学习开辟了令人振奋的新途径。然而,在运用MMLA创新时,通常会出现实际和后勤挑战。这些挑战可以包括与利用遥感能力提高学习空间有关的技术问题,以及教师任务和知情同意的日益复杂程度。这些实际问题很少讨论。本文件通过介绍与399名学生和17名教育工作者共同进行的为期两年的以人为中心的MMMLA网上研究所学到的一套经验教训来弥补这一差距。经验教训被综合纳入与以下有关的专题:部署的技术/物理方面;多式数据和界面;设计过程;参与、伦理和隐私;以及部署的可持续性。</s>